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Which Agent is More Captivating for Winning the Users' Hearts?: Focusing on Paralanguage Voice and Human-like Face Agent

  • Received : 2023.07.03
  • Accepted : 2024.04.24
  • Published : 2024.06.30

Abstract

This paper delves into the comparative analysis of human interactions with AI agents based on the presence or absence of a facial representation, combined with the presence or absence of paralanguage voice elements. The "CASA (Computer-Are-Social-Actors)" paradigm posits that people perceive computers as social actors, not tools, unconsciously applying human norms and behaviors to computers. Paralanguages are speech voice elements such as pitch, tone, stress, pause, duration, speed that help to convey what a speaker is trying to communicate. The focus is on understanding how these elements collectively contribute to the generation of flow, intimacy, trust, and interactional enjoyment within the user experience. Subsequently, this study uses PLS analysis to explore the connections among all variables within the research framework. This paper has academic and practical implications.

Keywords

Acknowledgement

This study was supported by a grant from Dong Yang University in 2021 & This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020S1A5A8041958)

References

  1. Adam, M., Wessel, M., and Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445. https://doi.org/10.1007/s12525-020-00414-7 
  2. Agarwal, R., and Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.  https://doi.org/10.2307/3250951
  3. Al-Azzawi, A. (2014). Experience with Technology: Dynamics of User Experience with Mobile Media Devices (Springer Briefs in Computer Science). London: Springer. 
  4. Aron, A., Aron, E. N., and Smollan, D. (1992). Inclusion of other in the self-scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596-612. https://doi.org/10.1037/0022-3514.63.4.596 
  5. Back, S. J., and Lee, Y. J. (2012). Game interface based on voice recognition for smartphone. In Korea Information Technology Association, Proceedings of KIIT Summer Conference. 
  6. Bartel, C. A., and Saavedra, R. (2000). The collective construction of work group moods. Administrative Science Quarterly, 45, 197-231. https://doi.org/10.2307/2667070 
  7. Bickmore, T. W. (2003). Relational Agents: Effecting Change Through Human-computer Relationships (Doctoral Dissertation). Massachusetts Institute of Technology, Boston, MA. 
  8. Bickmore, T. W., Fernando, R., Ring, L., and Schulman, D. (2010). Empathic touch by relational agents. IEEE Transactions on Affective Computing, 1(1), 60-71. https://doi.org/10.1109/T-AFFC.2010.4 
  9. Bickmore, T., and Cassell, J. (2001). Relational agents: A model and implementation of building user trust. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems CHI 01 (pp. 396-403). 
  10. Breazeal, C. (2003). Emotion and sociable humanoid robots. International Journal of Human-Computer Studies, 59(1), 119-155. https://doi.org/10.1016/S1071-5819(03)00018-1 
  11. Brill, T. M., Munoz, L., and Miller, R. J. (2022). Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications. In The Role of Smart Technologies in Decision Making (pp.1075-1084). Routledge. 
  12. Cacioppo, J. T., and Epley, N. (2010). Who sees human? The stability and importance of individual differences in anthropomorphism. Perspectives on Psychological Science, 5(3), 219-232.  https://doi.org/10.1177/1745691610369336
  13. Carroll, B. A., and Ahuvia, A. C. (2006). Some antecedents and outcomes of brand love. Marketing Letters, 17(2), 79-89. https://doi.org/10.1007/s11002-006-4219-2 
  14. Chandler, J., and Schwarz, N. (2010). Use does not wear ragged the fabric of friendship: Thinking of objects as a live makes people less willing to replace them. Journal of Consumer Psychology, 20(2), 138-145. https://doi.org/10.1016/j.jcps.2009.12.008 
  15. Chin, J., Diehl, V., and Norman, K. N. (1988). Development of an instrument measuring user satisfaction of the human-computer interface. In International Conference on Human Factors in Computing Systems. 
  16. Coeckelbergh, M. (2011). Humans, animals, and robots. International Journal of Social Robotics, 3(2), 197-204.  https://doi.org/10.1007/s12369-010-0075-6
  17. Coursaris, C. K., and Liu, M. (2009). An analysis of social support exchanges in online HIV/AIDS help groups. Computers in Human Behavior, 25(4), 911-918. https://doi.org/10.1016/j.chb.2009.03.006 
  18. Csikszentmihalyi, M. (1975). Play and intrinsic rewards. Journal of Humanistic Psychology, 15(3), 41-63. https://doi.org/10.1177/002216787501500306 
  19. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Journal of Leisure Research, 24(1), 93-94. https://doi.org/10.1080/00222216.1992.11969876 
  20. Csikszentmihalyi, M. (1991). Flow: The Psychology of Optimal Experience: Steps Toward Enhancing the Quality of Life. New York: Harper Collins Publishers. 
  21. Csikszentmihalyi, M. (1999). Flow: The Psychology of Optimal Experience. New York: Harper and Row. 
  22. Coursaris, C. K., and Liu, M. (2009). An analysis of social support exchanges in online HIV/AIDS self-help groups. Computers in Human Behavior, 25(4), 911-918. https://doi.org/10.1016/j.chb.2009.03.006 
  23. Daft, R. L., and Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554-571. https://doi.org/10.1287/mnsc.32.5.554 
  24. Dash, B., and Davis, K. (2022). Significance of nonverbal communication and paralinguistic featurs in communication: A critical analysis. International Journal for Innovative Research in Multidisciplinary Field, 8(4), 172-179. 
  25. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 
  26. Davis, R., and Wong, D. (2007). Conceptualizing and measuring the optimal experience of the e-learning environment. Decision Sciences Journal of Innovative Education, 5(1), 97-126. https://doi.org/10.1111/j.1540-4609.2007.00129.x 
  27. Dinev, T., and Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61-80. https://doi.org/10.1287/isre.1060.0080 
  28. Dolganov, A. G., and Letnev, K. Y. (2020). Informative modeling of subjective reality for intellectual anthropomorphic robots. Proceedings of the Materials Science and Engineering, 966(1), 012084. https://doi.org/10.1088/1757-899X/966/1/012084 
  29. Ene, I., Pop, M. I., and Nistoreanu, B. (2019). Qualitative and quantitative analysis of consumers perception regarding anthropomorphic AI designs. In Proceedings of the International Conference on Business Excellence, 13(1), 707-716. https://doi.org/10.2478/picbe-2019-0063 
  30. Gambino, A., Fox, J., and Ratan, R. A. (2020). Building a stronger CASA: Extending the computers are social actors paradigm. Human-Machine Communication, 1, 71-85. https://doi.org/10.30658/hmc.1.5 
  31. Gao, Y., Pan, Z., Wang, H., and Chen, G. (2018). Alexa, my love: Analyzing reviews of Amazon Echo. In 2018 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (Smart World/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 372-380). https://doi.org/10.1109/SmartWorld.2018.00094 
  32. Ginossar, T. (2008). Online participation: A content analysis of differences in utilization of two online cancer communities by men and women, patients and family members. Health, 23(1), 1-12. https://doi.org/10.1080/10410230701697100 
  33. Hessels, R. S., Holleman, G. A., Kingstone, A., Hooge, I. T., and Kemner, C. (2019). Gaze allocation in face-to-face communication is affected primarily by task structure and social context, not stimulus-driven factors. Cognition, 184, 28-43. https://doi.org/10.1016/j.cognition.2018.12.005 
  34. Ho, L. A., and Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees' computer attitude and flow experience. Computers in Human Behavior, 26(1), 23-31. https://doi.org/10.1016/j.chb.2009.07.007 
  35. Hoffman, D. L., and Novak, P. T. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. The Journal of Marketing, 60(3), 50-68. https://doi.org/10.2307/1251841 
  36. Hussein, Z. (2015). Explicating students' behaviours of e-learning: A viewpoint of the extended technology acceptance. International Journal of Management and Applied Science, 1(10), 68-73. 
  37. Hou, J., and Lee, K. M. (2011, October). Effects of self-conscious emotions on affective and responses in HCI and CMC. [Paper presentation] SIGDOC&11, Italy. 
  38. Hoy, M. B. (2018). Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants. Medical Reference Services Quarterly, 37(1), 81-88. https://doi.org/10.1080/0267257X.2019.1687571 
  39. Jiang, Z., and Benbasat, I. (2007). Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18(4), 454-470.  https://doi.org/10.1287/isre.1070.0124
  40. Johnson, W. L., Rickel, J. W., and Lester, J. C. (2000). Animated pedagogical agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 11(1), 47-78. 
  41. Kahai, S. S., and Cooper, R. B. (2003). Exploring the core concept of media richness theory: The impact of cue multiplicity and feedback immediacy on decision quality. Journal of Management Information Systems, 20(1), 263-299. https://doi.org/10.1080/07421222.2003.11045754 
  42. Kelshaw, T. (2016, October 5). AI & gender: A Maxus survey, Retrieved from http://www.maxusglobal.com/blog/ai-gender-maxus-survey 
  43. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223. http://www.jstor.org/stable/23011056  1056
  44. Lee, D. K., and Borah, P. (2020). Self-presentation on Instagram and friendship development among young adults: A moderated mediation model of media richness, perceived functionality, and openness. Computers in. Human. Behaviour, 103, 57-66. https://doi.org/10.1016/j.chb.2019.09.017 
  45. Lee, S. Y., and Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95-105. https://doi.org/10. 1016/j.ijhcs.2017.02.005  https://doi.org/10.1016/j.ijhcs.2017.02.005
  46. Lee, U. K. (2018). Accessing the acceptance of the speech recognition based virtual assistant service: Applying UTAUT model. Journal of Product Research, 38(5), 111-120. https://doi.org/10.1371/journal.pdig.0000510 
  47. Lee, J. R., and Nass, C. I. (2010). Trust in Computers: The Computers-Are-Social-Actors (CASA) Paradigm and Trustworthiness Perception in Human-Computer Communication. In D. Latusek and A. Gerbasi (Eds.), Trust and Technology in a Ubiquitous Modern Environment: Theoretical and Methodological Perspectives (pp. 1-15). IGI Global. https://doi.org/10.4018/978-1-61520-901-9.ch001 
  48. Leigh, T., and Summers, J. O. (2013). An initial evaluation of industrial buyers' impressions of salespersons' nonverbal cues. Journal of Personal Selling and Sales Management, 22(1), 41-53. https://doi.org/10.1080/08853134.2002.10754292 
  49. Lim, W. M., Kumar, S., Verma, S., and Chaturvedi, R. (2022). Alexa, what do we know about conversational commerce? Insights from a systematic literature review. Psychology & Marketing, 39(6), 1129-1155.  https://doi.org/10.1002/mar.21654
  50. Littlejohn, S. W., and Foss, K. A. (2010). Theories of Human Communication (10th ed.). Waveland Press. 
  51. Lukyanenko, R., Maass, W., and Storey, V.C. (2022). Trust in artificial intelligence: From a foundational trust framework to emerging research opportunities. Electron Markets, 32, 1993-2020. https://doi.org/10.1007/s12525-022-00605-4 
  52. Maxham, J. G. III. (2001). Service recovery's influence on consumer satisfaction, positive word-of-mouth, and purchase intentions. Journal of Business Research, 54(1), 11-24. https://doi.org/10.1016/S0148-2963(00)00114-4 
  53. Mayer, R. C., Davis, J. H., and Schoorman, F. D. (1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709-734. https://doi.org/10.2307/258792 
  54. Mehrabian, A. (1969). Significance of posture and position in the communication of attitude and status relationships. Psychological Bulletin, 71(5), 359-372. https://doi.org/10.1037/h0027349 
  55. Mehrabian, A. (1971). Silent messages (Vol. 8, No. 152, p. 30). Belmont, CA: Wadsworth. 
  56. Minsky, M. (2007), The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon & Schuster, New York. 
  57. Mhatre, N., Motani, K., Shah, M., and Mali, S. (2016). Donna interactive chat-bot acting as a personal assistant. International Journal of Computer Applications, 140(10), p.6-11. https://doi.org/10.5120/ijca2016909460. 
  58. Moon, Y., and Nass, C. (1996). How "real" are computer personalities? Psychological responses to personality types in human-computer interaction. Communication Research, 23(6), 651-674. https://doi.org/10.1177/009365096023006002 
  59. Moorman, C., Deshpande, R., and Zaltman, G. (1993). Factors affecting trust in market research relationships. The Journal of Marketing, 57, 81-101. https://doi.org/10.2307/1252059 
  60. Morgan, R., and Hunt, S. (1994). The Commitment-Trust Theory of Relationship Marketing. The Journal of Marketing, 58, 20-38. http://dx.doi.org/10.2307/1252308 
  61. Mori, M. (1970). The uncanny valley. Energy, 7(4), 33-35 
  62. Morkes, J., Kernal, H. K., and Nass, C., (1998). Humor in task-orientated computer-mediated communication and human computer interaction. Conference on Human Factors in Computing Systems. Los Angeles. 
  63. Nass, C., and Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81-103.  https://doi.org/10.1111/0022-4537.00153
  64. Novak, T. P., Hoffman, D. L., and Yung, Y. F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42. http://www.jstor.org/stable/193257  https://doi.org/10.1287/mksc.19.1.22.15184
  65. Nowak, K. L., and Rauh, C. (2005). The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction. Journal of Computer- Mediated Communication, 11(1), 153-178. https://doi.org/10.1111/j.1083-6101.2006.tb00308.x 
  66. Nowak, K. L., and Rauh, C. (2008). Choose your "buddy icon" carefully: The influence of avatar androgyny, anthropomorphism and credibility in online interactions. Computers in Human Behavior, 24(4), 1473-1493. https://doi.org/10.1016/j.chb.2007.05.005 
  67. Orr, D. A., and Sanchez, L. (2018). Alexa, did you get that? Determining the evidentiary value of data stored by the Amazon Echo. Digital Investigation, 24, 72-78. https://doi.org/10.1016/j.diin.2017.12.002. 
  68. Park, J., Son, H., Lee, J., and Choi, J. (2018). Driving assistant companion with voice interface using long short-term memory networks. Transactions on Industrial Informatics, 15(1), 582-590.  https://doi.org/10.1109/TII.2018.2861739
  69. Pelau, C., Volkmann, C., Barbul, M., and Bojescu, I. (2023). The role of attachment in improving consumer-AI interactions. Proceedings of the International Conference on Business Excellence, 17, 1075-1084. https://doi.org/10.2478/picbe-2023-0097 
  70. Peters, D., Calvo, R. A., and Ryan, R. M. (2018). Designing for motivation, engagement and wellbeing in digital experience. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00797 
  71. Picard, R. W. (2003). Affective computing: challenges. International Journal of Human Computer Studies, 59(1-2), 55-64. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S1071581903000521  1071581903000521
  72. Picard, R. W. (2008). Toward machines with emotional intelligence. In G. Matthews, M. Zeidner, and R. D. Roberts (Eds.), The Science of Emotional Intelligence: Knowns and Unknowns, Series in Affective Science. New York: Oxford Academic. https://doi.org/10.1093/acprof:oso/9780195181890.003.0016. 
  73. Qiu, L., and Benbasat, I. (2009). Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. Journal of Management Information Systems, 25(4), 145-182. https://doi.org/10.2753/MIS0742-1222250405 
  74. Rafaeli, S. (1988). Interactivity: From new media to communication. In R. P. Hawkins, J. M. Wiemann, and S. Pingree (Eds.), Sage Annual Review of Communication Research: Advancing Communication Science (Vol. 16, pp. 110-134). Beverly Hills, CA: Sage. https://doi.org/10.1111/j.1083-6101.1997.tb00201.x 
  75. Reeves, B., and Nass, C. I. (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Center for the Study of Language and Information; Cambridge University Press. 
  76. Rempel, J., Holmes, J., and Zanna, M. (1985). Trust in close relationships. Journal of Personality and Social Psychology, 49, 95-112. https://psycnet.apa.org/doi/10.1037/0022-3514.49.1.95 
  77. Ruel, H., and Njoku, E. (2021), AI redefining the hospitality industry. Journal of Tourism Futures, 7(1), 53-66. https://doi.org/10.1108/JTF-03-2020-0032 
  78. Song, Y., and Luximon, Y. (2020). Trust in AI agent: A systematic review of facial anthropomorphic trustworthiness for social robot design. Sensors, 20(18), 5087. 
  79. Suh, K. S., and Lee, Y. E. (2005). The effects of virtual reality on consumer learning: An empirical investigation. MIS Quarterly, 29(4), 673-697.  https://doi.org/10.2307/25148705
  80. Sundar, S. S., and Kim, J. (2005). Interactivity and persuasion: Influencing attitudes with information and involvement. Journal of Interactive Advertising, 5(2), 5-18.  https://doi.org/10.1080/15252019.2005.10722097
  81. Sproull, L., Subramani, M., Kiesler, S., Walker, J. H., and Waters, K. (1996). When the interface is a face. Human-Computer Interaction, 11(2), 97-124. https://doi.org/10.1207/s15327051hci1102_1 
  82. Littlejohn, S. W., and Foss, K. A. (Eds.) (2009). Encyclopedia of Communication Theory. Thousand Oaks, Calif: Sage. 
  83. Tepper, D. T., and Haase, R. F. (1978). Verbal and nonverbal communication of facilitative conditions. Journal of Counseling Psychology, 25(1), 35-44. https://doi.org/10.1037/0022-0167.25.1.35 
  84. Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information & Management, 40, 541-549. https://doi.org/10.1016/S0378-7206(02)00079-4. 
  85. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704. https://doi.org/10.2307/25148660 
  86. Wainwright, M. J. (1999). Visual adaptation as optimal information transmission. Vision Research, 19(23), 3960-3974. https://doi.org/10.1016/S0042-6989(99)00101-7. 
  87. Wang, W., and Benbasat, I. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 72-101.  https://doi.org/10.17705/1jais.00065
  88. Walker, J., Sproull, L., and Subramani, M. (1994). Using a human face in an interface. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 85-91). https://doi.org/10.1145/259963.260290. 
  89. Waytz, A., Cacioppo, A., and Epley, N. (2010). Who sees human? The stability and importance of individual differences in anthropomorphism. Perspectives on Psychological Science, 5(3), 219-232.  https://doi.org/10.1177/1745691610369336
  90. Zanbaka, C., Goolkasian, P., and Hodges, L. (2006). Can a virtual cat persuade you? The role of gender and realism in speaker persuasiveness. In CHI 2006(pp. 1153-1162). https://doi.org/10.1145/1124772.1124945 
  91. Zhu, L., Benbasat, I., and Jiang, Z. (2010). Let's shop online together: An empirical investigation of collaborative online shopping support. Information Systems Research, 21(4), 872-891. https://doi.org/10.1287/isre.1080.0218